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[fx] provide a stable but not accurate enough version of profiler. (#1547)

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* [fx] compute memory stat and flop count for MetaInfoProp.

* [fx] modify node attribute.

* [fx] modify ckpt_chen.

* [fx] fix compatibility.

* [fx] fix import error.

* [fx] skip test for MetaInfoProp.

* [fx] skip test for MetaInfoProp.

* [fx] skip test for MetaInfoProp.

* [fx] skip test for MetaInfoProp.

* [fx] skip if torch 1.11.0.

* [fx] recover MetaInfoProp support for PyTorch 1.11.

* [fx] provide a stable but not accurate enough version of profiler.

* [fx] provide a stable but not accurate enough version of profiler.

* [fx] fix compatibility in tests.

* [fx] fix compatibility in tests.

* [fx] fix compatibility in tests.

* [fx] fix compatibility in tests.

* [fx] fix compatibility in tests.

* [fx] fix compatibility in tests.

* [fx] fix compatibility in tests.

* [fx] fix compatibility in tests.

* [fx] fix compatibility in tests.

* [fx] fix compatibility in tests.

* [fx] fix import error.
This commit is contained in:
Super Daniel
2022-09-07 11:21:04 +08:00
committed by GitHub
parent 7d49e7b2db
commit 4f59693207
38 changed files with 776 additions and 263 deletions
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22
colossalai/fx/profiler/experimental/profiler_module/pooling.py Normal file
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from typing import Tuple
import torch
from ..registry import meta_profiler_module
@meta_profiler_module.register(torch.nn.AvgPool1d)
@meta_profiler_module.register(torch.nn.AvgPool2d)
@meta_profiler_module.register(torch.nn.AvgPool3d)
@meta_profiler_module.register(torch.nn.MaxPool1d)
@meta_profiler_module.register(torch.nn.MaxPool2d)
@meta_profiler_module.register(torch.nn.MaxPool3d)
@meta_profiler_module.register(torch.nn.AdaptiveAvgPool1d)
@meta_profiler_module.register(torch.nn.AdaptiveMaxPool1d)
@meta_profiler_module.register(torch.nn.AdaptiveAvgPool2d)
@meta_profiler_module.register(torch.nn.AdaptiveMaxPool2d)
@meta_profiler_module.register(torch.nn.AdaptiveAvgPool3d)
@meta_profiler_module.register(torch.nn.AdaptiveMaxPool3d)
def torch_nn_pooling(self: torch.nn.Module, input: torch.Tensor) -> Tuple[int, int]:
# all pooling could be considered as going over each input element only once (https://stackoverflow.com/a/67301217)
flops = input.numel()
macs = 0
return flops, macs